Regularization:  First, as in [1]}, we add weight regualarization during inner loop training of the form \(\epsilon _w \sum _{w \in \mathcal {W}} \Vert w\Vert \) , where \(\mathcal {W}\)  denotes the set of weight parameters, where \(\epsilon _w = 0.01\) . In PyTorch, this is expressed as
